To batch process documents efficiently with embedding models, you need to optimize data handling, parallelize computations, and manage hardware resources effectively. Start by grouping documents into batches that fit your model's maximum input capacity (e.g., 32 or 64 texts per batch) to minimize overhead from repeated model calls. Use libraries like Hugging Face Transformers or sentence-transformers, which support batch inference out of the box. For example, with sentence-transformers, you can pass a list of strings to model.encode() to generate embeddings for all texts in a single forward pass. This approach reduces latency compared to processing one document at a time, especially when using GPUs, which excel at parallel computations.
Hardware and preprocessing play a critical role. GPUs like NVIDIA A100s or consumer-grade cards (e.g., RTX 3090) significantly speed up batch processing due to their parallel processing capabilities. If using CPUs, leverage multithreading via libraries like NumPy or PyTorch, which can split batches across cores. Preprocess documents in advance to ensure consistent input formats: remove extraneous characters, truncate or pad text to match the model's token limit (e.g., 512 tokens for BERT), and store preprocessed data in memory-efficient formats like parquet or memory-mapped arrays. For large datasets, use a data loader (e.g., PyTorch's DataLoader) to stream batches from disk instead of loading all data into memory. For example, you could process a 10,000-document dataset in batches of 64, iterating through chunks without overwhelming RAM.
Error handling and scalability are also key. Implement retry logic for failed batches, and use a queue system (e.g., Redis or RabbitMQ) to manage large-scale workflows. Monitor GPU/CPU usage with tools like nvidia-smi or htop to avoid bottlenecks. If latency is critical, consider model optimizations like quantization (reducing precision from 32-bit to 16-bit floats) or using smaller models (e.g., all-MiniLM-L6-v2 instead of larger variants). Finally, store embeddings in a vector database like FAISS or Pinecone for fast retrieval. For example, after generating embeddings for a batch, you could immediately index them in FAISS to build a search system incrementally. This end-to-end approach balances speed, resource usage, and practicality for real-world applications.